RQs-Research focus






Raw data







Feature level








Pattern level




Analytical technique









Insight about learning





Paper ID title authors' keywords Full RQs RQs-Research focus learning-construct Raw data Feature level Pattern level Analytical technique Insight about learning year Authors
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-forum Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1 A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics Learning analytics; Learning tactic; Process model; Self-regulated learning RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2021 Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Non-srl.indicators.identification other Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Non-srl.indicators.identification other Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Non-srl.indicators.identification other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Non-srl.indicators.identification other Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Group.comparison other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Group.comparison other Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Group.comparison other Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Group.comparison other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2 Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? Group.comparison other Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
3 Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data Human tutoring studies; Learning analtyics; Multimodal How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? Time.to.intervention other Multimodal Event Summative Basic.statistical.analysis Time.on.learning 2021 Chen, Lujie Karen
3 Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data Human tutoring studies; Learning analtyics; Multimodal How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? Time.to.intervention other Multimodal Trace-feedback Summative Basic.statistical.analysis Time.on.learning 2021 Chen, Lujie Karen
3 Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data Human tutoring studies; Learning analtyics; Multimodal How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? Time.to.intervention other Multimodal Time Summative Basic.statistical.analysis Time.on.learning 2021 Chen, Lujie Karen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Content.analysis Feedback 2021 Lee, Alwyn Vwen Yen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Content.analysis Collaboration 2021 Lee, Alwyn Vwen Yen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Network.analysis Feedback 2021 Lee, Alwyn Vwen Yen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Network.analysis Collaboration 2021 Lee, Alwyn Vwen Yen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Cluster.analysis Feedback 2021 Lee, Alwyn Vwen Yen
4 Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” Exploring.socio-dynamics context costumization Contextual Trace-other Group.event.pattern Cluster.analysis Collaboration 2021 Lee, Alwyn Vwen Yen
5 Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5 Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5 Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? Method.development SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2021 Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5 Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? Method.development SRL Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
6 SAINT+: Integrating Temporal Features for EdNet Correctness Prediction Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer None Method.development None Lms.log.data Event None Neural.network Course.design 2021 Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
6 SAINT+: Integrating Temporal Features for EdNet Correctness Prediction Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer None Method.development None Lms.log.data Time None Neural.network Course.design 2021 Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
7 Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation Bayesian analysis; Learning; Online instruction None Method.development other Lms.log.data Event Transitional.pattern Process.mining Course.design 2021 Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7 Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation Bayesian analysis; Learning; Online instruction None Method.development other Lms.log.data Trace-reading Transitional.pattern Process.mining Course.design 2021 Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7 Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation Bayesian analysis; Learning; Online instruction None Method.development other Lms.log.data Trace-quiz Transitional.pattern Process.mining Course.design 2021 Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7 Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation Bayesian analysis; Learning; Online instruction None Method.development other Lms.log.data Trace-forum Transitional.pattern Process.mining Course.design 2021 Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
8 Temporality revisited: Dynamicity issues in collaborative digital writing research Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like? Method.development collaborative knowledge building Learning.product Trace-forum None Content.analysis Collaboration 2021 Engerer, Volkmar P.
8 Temporality revisited: Dynamicity issues in collaborative digital writing research Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like? Method.development collaborative knowledge building Learning.product Trace-forum None Content.analysis Learning.indicators 2021 Engerer, Volkmar P.
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Event Transitional.pattern Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Time Other.sequential.patterns Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Time Transitional.pattern Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Trace-quiz Other.sequential.patterns Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9 Temporal Cross-Effects in Knowledge Tracing collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) Method.development knowledge tracing Performance.measures Trace-quiz Transitional.pattern Process.mining Time.on.learning 2021 Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Event Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-reading Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Event Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Event Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Trace-reading Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10 Theory-based learning analytics to explore student engagement patterns in a peer review activity Peer reviews; learning analytics; process mining; student engagement How can theory-informed LA help identify and interpret engagement patterns in peer reviews? Exploring.srl.processes SRL; SSRL Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
11 Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? Group.comparison context costumization Customized.log.data Event Summative Cluster.analysis Time.on.learning 2021 Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11 Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? Group.comparison context costumization Customized.log.data Time Summative Cluster.analysis Time.on.learning 2021 Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11 Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? Group.comparison context costumization Performance.measures Event Summative Cluster.analysis Time.on.learning 2021 Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11 Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? Group.comparison context costumization Performance.measures Time Summative Cluster.analysis Time.on.learning 2021 Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Event Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? Group.comparison None Learning.product Time Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Event Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Transitional.pattern Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Summative Cluster.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Summative Process.mining No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12 Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? At-risk.student.identification None Learning.product Time Summative Visualization.analysis No.learning.focus.outcome 2021 Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-video Summative Qualitative.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-video Summative Qualitative.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-video Summative Visualization.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-video Summative Visualization.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-feedback Summative Qualitative.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-feedback Summative Qualitative.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-feedback Summative Visualization.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-feedback Summative Visualization.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-other Summative Qualitative.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-other Summative Qualitative.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-other Summative Visualization.analysis Course.design 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13 A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics Knowledge acquisition; Medical students; Memory Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. Non-srl.indicators.identification other Learning.product Trace-other Summative Visualization.analysis Feedback 2021 McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Event Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Event Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Trace-reading Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Trace-reading Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Time Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Lms.log.data Time Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Event Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Event Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Trace-reading Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Trace-reading Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Time Summative Cluster.analysis Course.design 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14 Time-driven modeling of student self-regulated learning in Network analysis-based tutors Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? Exploring.srl.processes SRL Self-reported Time Summative Cluster.analysis Time.on.learning 2021 Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Transitional.pattern Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Transitional.pattern Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Transitional.pattern Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Summative Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Summative Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Event Summative Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Transitional.pattern Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Transitional.pattern Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Summative Basic.statistical.analysis Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Summative Basic.statistical.analysis Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Summative Process.mining Learning.indicators 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15 Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. Exploring.socio-dynamics collaborative knowledge building Self-reported Trace-forum Summative Process.mining Collaboration 2021 Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
16 Using process mining for Git log analysis of projects in a software development course Computer Appl. in Social and Behavioral Sciences; Computers and Education; Education; Educational Technology; Information Systems Applications (incl.Internet); User Interfaces and Human Computer Interaction; general RQ1: what are the features to extract from the Git log data, and how should be data be processed in order to be usable in the process mining analysis of project development? RQ2: what are the characteristics of the project development process form the perpective of the Git log attributes? RQ3: what are the benefits and limitation of process mining in the Git log analysis of student projects? Method.development None Customized.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Macak, Martin, Kruzelova, Daniela, Chren, Stanislav, Buhnova, Barbora
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Customized.log.data Event None Neural.network Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Customized.log.data Event None Visualization.analysis Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Customized.log.data Time None Neural.network Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Customized.log.data Time None Visualization.analysis Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Learning.product Event None Neural.network Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Learning.product Event None Visualization.analysis Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Learning.product Time None Neural.network Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
17 Variational Deep Knowledge Tracing for Language Learning deep learning; knowledge tracing; language learning; student modeling; variational inference None Method.development None Learning.product Time None Visualization.analysis Course.design 2021 Ruan, Sherry, Wei, Wei, Landay, James
18 Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA? Non-srl.indicators.identification other Self-reported Trace-forum Summative Basic.statistical.analysis Learning.indicators 2021 Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
18 Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA? Method.development other Self-reported Trace-forum Summative Basic.statistical.analysis Learning.indicators 2021 Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
19 Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics addressing the problem of inflated values in finding significance in transitions Method.development affective learning Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2021 Matayoshi, Jeffrey, Karumbaiah, Shamya
19 Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics addressing the problem of inflated values in finding significance in transitions Method.development affective learning Lms.log.data Trace-other Transitional.pattern Process.mining No.learning.focus.outcome 2021 Matayoshi, Jeffrey, Karumbaiah, Shamya
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-feedback Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Event Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-feedback Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20 Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? Exploring.srl.processes SRL; SSRL Learning.product Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2021 Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Trace-other Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Trace-other Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Trace-feedback Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Customized.log.data Trace-feedback Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Event Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Event Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Trace-other Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Trace-other Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Trace-feedback Transitional.pattern Process.mining Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
21 Towards the successful game-based learning: Detection and feedback to misconceptions is the key Elementary education; Games; Teaching/learning strategies (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? Non-srl.indicators.identification game-based learning Learning.product Trace-feedback Transitional.pattern Basic.statistical.analysis Feedback 2021 Yang, Kai-Hsiang, Lu, Bou-Chuan
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Event.sequence Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Event.sequence Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Event.sequence Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
22 Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Log files; Process.mining; Self-regulated learning; TPACK 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? Exploring.srl.processes SRL Learning.product Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2021 Huang, Lingyun, Lajoie, Susanne P
23 Predicting student success in a blended learning environment blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification None At-risk.student.identification None Lms.log.data Event Summative Basic.statistical.analysis No.learning.focus.outcome 2020 Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
23 Predicting student success in a blended learning environment blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification None At-risk.student.identification None Lms.log.data Time Summative Basic.statistical.analysis No.learning.focus.outcome 2020 Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Method.development cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Event.sequence Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Process.mining Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24 Supporting actionable intelligence: reframing the analysis of observed study strategies explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? Group.comparison cognitive activities (learning actions) Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2020 Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Lms.log.data Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-reading Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Event.sequence Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Event.sequence Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Event.sequence Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Event.sequence Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Group.event.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Group.event.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Transitional.pattern Process.mining Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Transitional.pattern Network.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25 Analytics of time management and learning strategies for effective online learning in blended environments blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? Method.development SRL Performance.measures Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
26 How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping None Method.development collaborative knowledge building Learning.product Event Summative Network.analysis Collaboration 2020 Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26 How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping None Method.development collaborative knowledge building Learning.product Event Summative Network.analysis Learning.indicators 2020 Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26 How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping None Method.development collaborative knowledge building Learning.product Trace-forum Summative Network.analysis Collaboration 2020 Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26 How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping None Method.development collaborative knowledge building Learning.product Trace-forum Summative Network.analysis Learning.indicators 2020 Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Lms.log.data Time Other.sequential.patterns Neural.network No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Lms.log.data Time Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Performance.measures Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Performance.measures Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Performance.measures Time Other.sequential.patterns Neural.network No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
27 A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction None None At-risk.student.identification None Performance.measures Time Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Qiao, Chen, Hu, Xiao
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28 Process.mining for self-regulated learning assessment in e-learning e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2020 Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-quiz Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Exploring.srl.processes SRL Performance.measures Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-quiz Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Event.sequence Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29 Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. Method.development SRL Performance.measures Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Visualization.analysis Time.on.learning 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Learning.product Trace-feedback Other.sequential.patterns Visualization.analysis Time.on.learning 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Learning.product Trace-feedback Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Self-reported Trace-forum Other.sequential.patterns Visualization.analysis Time.on.learning 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Self-reported Trace-forum Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Self-reported Trace-feedback Other.sequential.patterns Visualization.analysis Time.on.learning 2020 Engerer, Volkmar P.
30 Implementing dynamicity in research designs for collaborative digital writing Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? Method.development collaborative knowledge building Self-reported Trace-feedback Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Engerer, Volkmar P.
31 Predicting Learners' Effortful Behaviour in Adaptive Assessment Using Multimodal Data adaptive assessment; effort classification; hidden Markov models; multimodal learning analytics RQ: How can we predict learners’ effort using multimodal data? Method.development other Multimodal Event Summative Cluster.analysis Time.on.learning 2020 Sharma, Kshitij, Papamitsiou, Zacharoula, Olsen, Jennifer K, Giannakos, Michail
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32 Analytics of Learning Strategies: Role of Course Design and Delivery Modality Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? Method.development SRL Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2020 Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
33 Prediction of students’ early dropout based on their interaction logs in online learning environment Prediction; extract feature; input-output hidden Markov model; logistic regression; machine learning; online learning environment None At-risk.student.identification None Lms.log.data Event Summative Basic.statistical.analysis No.learning.focus.outcome 2020 Mubarak, Ahmed A., Cao, Han, Zhang, Weizhen
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Group.comparison collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Group.comparison collaborative knowledge building Lms.log.data Event Summative Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Group.comparison collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Group.comparison collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
34 Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Collaboration 2020 Wu, Sheng Yi, Wang, Shu Ming
35 Reply to which post? An analysis of peer reviews in a high school SPOC Peer review; SPOC; high school; online interaction; social Network analysis analysis what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? Exploring.socio-dynamics collaborative knowledge building; feedback Learning.product Event Summative Basic.statistical.analysis Time.on.learning 2020 Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35 Reply to which post? An analysis of peer reviews in a high school SPOC Peer review; SPOC; high school; online interaction; social Network analysis analysis what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? Exploring.socio-dynamics collaborative knowledge building; feedback Learning.product Event Summative Basic.statistical.analysis Feedback 2020 Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35 Reply to which post? An analysis of peer reviews in a high school SPOC Peer review; SPOC; high school; online interaction; social Network analysis analysis what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? Exploring.socio-dynamics collaborative knowledge building; feedback Learning.product Trace-forum Summative Basic.statistical.analysis Time.on.learning 2020 Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35 Reply to which post? An analysis of peer reviews in a high school SPOC Peer review; SPOC; high school; online interaction; social Network analysis analysis what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? Exploring.socio-dynamics collaborative knowledge building; feedback Learning.product Trace-forum Summative Basic.statistical.analysis Feedback 2020 Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Group.comparison game-based learning Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Group.comparison game-based learning Customized.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Group.comparison game-based learning Customized.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Group.comparison game-based learning Customized.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Non-srl.indicators.identification game-based learning Customized.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
36 Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game Board game; Computational participation; Computational thinking; Unplugged How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? Non-srl.indicators.identification game-based learning Customized.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Kuo, Wei Chen, Hsu, Ting Chia
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Event Transitional.pattern Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Event Summative Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Trace-other Transitional.pattern Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Trace-other Summative Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Customized.log.data Trace-other Summative Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Event Transitional.pattern Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Event Summative Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Trace-other Transitional.pattern Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Trace-other Summative Network.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37 Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? Exploring.srl.processes SRL Self-reported Trace-other Summative Basic.statistical.analysis Learning.indicators 2020 Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Lms.log.data Trace-reading Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Lms.log.data Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Self-reported Event Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Self-reported Trace-reading Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38 Prediction of learners’ dropout in E-learning based on the unusual behaviors Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors None At-risk.student.identification SRL Self-reported Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2020 Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Summative Content.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Summative Content.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Summative Network.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Summative Network.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Transitional.pattern Content.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Transitional.pattern Content.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Transitional.pattern Network.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Event Transitional.pattern Network.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Summative Content.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Summative Content.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Summative Network.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Summative Network.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Transitional.pattern Content.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Transitional.pattern Content.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Transitional.pattern Network.analysis Time.on.learning 2020 Chen, Bodong, Poquet, Oleksandra
39 Socio-Temporal Dynamics in Peer Interaction Events digital peer Network analysiss; relational event modelling; temporality What are the mechanisms of social interaction in asynchronous online discussions? Exploring.socio-dynamics social interactions Customized.log.data Trace-forum Transitional.pattern Network.analysis Collaboration 2020 Chen, Bodong, Poquet, Oleksandra
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Summative Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Summative Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Summative Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Transitional.pattern Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Summative Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Summative Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Summative Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Summative Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Summative Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Summative Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Transitional.pattern Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Summative Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Summative Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Summative Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Network.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40 Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? Method.development SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2020 Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
41 Reinforcement Learning for the Adaptive Scheduling of Educational Activities adaptive learning; online education; reinforcement learning R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? Method.development None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41 Reinforcement Learning for the Adaptive Scheduling of Educational Activities adaptive learning; online education; reinforcement learning R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? Method.development None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2020 Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41 Reinforcement Learning for the Adaptive Scheduling of Educational Activities adaptive learning; online education; reinforcement learning R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41 Reinforcement Learning for the Adaptive Scheduling of Educational Activities adaptive learning; online education; reinforcement learning R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2020 Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
42 Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities? Non-srl.indicators.identification collaborative knowledge building; other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
42 Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities? Non-srl.indicators.identification collaborative knowledge building; other Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2020 Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Lms.log.data Trace-other Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Event Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-reading Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Self-reported Trace-other Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Event.sequence Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Frequent.sequence.mining Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Cluster.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43 How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? Exploring.srl.processes SRL Performance.measures Trace-other Group.event.pattern Visualization.analysis Course.design 2020 Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Event Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Event Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Trace-reading Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Multimodal Trace-quiz Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Event Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Trace-reading Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Exploring.srl.processes SSRL; collaborative knowledge building Self-reported Trace-quiz Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Event Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Event Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Trace-reading Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Multimodal Trace-quiz Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Event Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Trace-reading Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44 How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? Group.comparison SSRL; collaborative knowledge building Self-reported Trace-quiz Summative Visualization.analysis Learning.indicators 2020 Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Self-reported Event Summative Basic.statistical.analysis Collaboration 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Performance.measures Event Summative Basic.statistical.analysis Collaboration 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Multimodal Event Summative Basic.statistical.analysis Collaboration 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45 Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? Exploring.srl.processes SRL Multimodal Event Summative Basic.statistical.analysis Learning.indicators 2020 Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Time Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-video Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Lms.log.data Trace-quiz Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Time Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-video Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-reading Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Summative Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Summative Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Summative Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Transitional.pattern Process.mining Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Transitional.pattern Process.mining Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Transitional.pattern Basic.statistical.analysis Learning.indicators 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46 The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? Method.development collaborative knowledge building Contextual Trace-quiz Transitional.pattern Basic.statistical.analysis Course.design 2020 Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
47 Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences learning analytics; measurement; outlier detection; temporal analysis; time-on-task This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. Method.development other Lms.log.data Event Other.sequential.patterns Other.predictions.models Learning.indicators 2020 Nguyen, Quan
47 Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences learning analytics; measurement; outlier detection; temporal analysis; time-on-task This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. Method.development other Lms.log.data Event Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Nguyen, Quan
47 Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences learning analytics; measurement; outlier detection; temporal analysis; time-on-task This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. Method.development other Lms.log.data Time Other.sequential.patterns Other.predictions.models Learning.indicators 2020 Nguyen, Quan
47 Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences learning analytics; measurement; outlier detection; temporal analysis; time-on-task This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. Method.development other Lms.log.data Time Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Nguyen, Quan
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Customized.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Customized.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Customized.log.data Trace-exercise Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Customized.log.data Trace-exercise Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Performance.measures Trace-exercise Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48 Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing deep learning; education; knowledge tracing; personalized learning; transformer We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations Method.development knowledge tracing Performance.measures Trace-exercise Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Customized.log.data Event Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Customized.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Customized.log.data Trace-exercise Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Customized.log.data Trace-exercise Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Performance.measures Trace-exercise Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
49 RKT: Relation-Aware Self-Attention for Knowledge Tracing attention Network analysiss; educational data mining; knowledge tracing; relation-aware model we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student Method.development knowledge tracing Performance.measures Trace-exercise Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2020 Pandey, Shalini, Srivastava, Jaideep
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Lms.log.data Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Learner.characteristics Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Learner.characteristics Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? Method.development None Performance.measures Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Lms.log.data Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Learner.characteristics Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Learner.characteristics Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
50 Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? At-risk.student.identification None Performance.measures Event Event.sequence Neural.network Learning.indicators 2020 Malekian, Donia, Bailey, James, Kennedy, Gregor
51 In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation Online discussion modeling; Persuasion; Social media RQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion? Method.development collaborative knowledge building Learning.product Event Summative Other.predictions.models Collaboration 2020 Guo, Zhen, Zhang, Zhe, Singh, Munindar
51 In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation Online discussion modeling; Persuasion; Social media RQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion? Method.development collaborative knowledge building Learning.product Trace-forum Summative Other.predictions.models Collaboration 2020 Guo, Zhen, Zhang, Zhe, Singh, Munindar
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Lms.log.data Event Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Lms.log.data Event Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Lms.log.data Trace-forum Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Learning.product Event Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Learning.product Event Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Learning.product Trace-forum Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Performance.measures Event Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Performance.measures Event Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Performance.measures Trace-forum Other.sequential.patterns Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
52 High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily None Method.development collaborative knowledge building Performance.measures Trace-forum Summative Network.analysis Time.on.learning 2020 Saqr, Mohammed, Nouri, Jalal
53 Exploring the Affordances of Sequence Mining in Educational Games Educational games; game-based assessment; learning analytics; sequence mining To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene. Method.development game-based learning Customized.log.data Event Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
53 Exploring the Affordances of Sequence Mining in Educational Games Educational games; game-based assessment; learning analytics; sequence mining To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene. Method.development game-based learning Customized.log.data Trace-other Other.sequential.patterns Visualization.analysis Learning.indicators 2020 Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Event Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Trace-feedback Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Trace-video Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Trace-reading Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Trace-exercise Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Customized.log.data Trace-other Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Event Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Trace-feedback Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Trace-video Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Trace-reading Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Trace-exercise Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
54 Analyzing Students' Behavior in Blended Learning Environment for Programming Education Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? Non-srl.indicators.identification feedback engagement Learner.characteristics Trace-other Summative Basic.statistical.analysis Feedback 2020 Luo, Jiwen, Wang, Tao
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Event Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Event Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Time Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Time Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-video Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-video Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-exercise Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-exercise Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-other Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Lms.log.data Trace-other Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Event Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Event Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Time Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Time Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-video Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-video Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-exercise Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-exercise Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-other Summative Cluster.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55 The importance and meaning of session behaviour in a MOOC Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? Exploring.srl.processes SRL Self-reported Trace-other Summative Visualization.analysis Learning.indicators 2020 de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Lms.log.data Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Self-reported Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Self-reported Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Self-reported Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Method.development SRL Self-reported Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Lms.log.data Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Self-reported Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Self-reported Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Self-reported Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In At-risk.student.identification SRL Self-reported Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Lms.log.data Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Self-reported Event Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Self-reported Trace-video Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Self-reported Trace-exercise Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56 Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In Exploring.srl.processes SRL Self-reported Time Summative Other.predictions.models No.learning.focus.outcome 2020 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
57 Temporal analysis of multimodal data to predict collaborative learning outcomes collaborative learning; learning analytics; multimodal (RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others? Method.development collaborative knowledge building Multimodal Event None Other.predictions.models No.learning.focus.outcome 2020 Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
57 Temporal analysis of multimodal data to predict collaborative learning outcomes collaborative learning; learning analytics; multimodal (RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others? Method.development collaborative knowledge building Multimodal Trace-other None Other.predictions.models No.learning.focus.outcome 2020 Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
58 Towards Understanding the Lifespan and Spread of Ideas: Epidemiological Modeling of Participation on Twitter connectivism; engagement patterns; epidemiology; ideas; knowledge creation; Network analysised learning In this paper, we present preliminary work of tackling this challenge by applying epidemiological modeling to the evolution of ideas. Method.development collaborative knowledge building Learning.product Event Summative Basic.statistical.analysis No.learning.focus.outcome 2020 Peri, Sai Santosh Sasank, Chen, Bodong, Dougall, Angela Liegey, Siemens, George
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Method.development other Performance.measures Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Event Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Time Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Group.event.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Group.event.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Transitional.pattern Cluster.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Transitional.pattern Process.mining Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59 Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? Group.comparison other Performance.measures Trace-quiz Transitional.pattern Visualization.analysis Time.on.learning 2020 Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
60 CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019) MOOCs; deep learning; dropout prediction; learning analytics None Method.development None Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
60 CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019) MOOCs; deep learning; dropout prediction; learning analytics None Method.development None Lms.log.data Time Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
61 Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study assessment; conceptual change; eye-tracking; visual attention H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG. Non-srl.indicators.identification other Multimodal Event Transitional.pattern Process.mining No.learning.focus.outcome 2019 Jin, Laipeng, Yu, Dongchuan
61 Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study assessment; conceptual change; eye-tracking; visual attention H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG. Non-srl.indicators.identification other Multimodal Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Jin, Laipeng, Yu, Dongchuan
62 An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis None Method.development None Lms.log.data Event None Other.predictions.models No.learning.focus.outcome 2019 Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62 An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis None Method.development None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2019 Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62 An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis None At-risk.student.identification None Lms.log.data Event None Other.predictions.models No.learning.focus.outcome 2019 Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62 An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis None At-risk.student.identification None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2019 Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
63 Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks deep learning; embeddings None Method.development None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2019 Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
63 Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks deep learning; embeddings None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2019 Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Trace-exercise Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Trace-video Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Trace-reading Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Lms.log.data Time Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Trace-exercise Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Trace-video Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Trace-reading Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Performance.measures Time Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Trace-exercise Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Trace-video Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Trace-reading Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64 Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. Non-srl.indicators.identification other Self-reported Time Summative Basic.statistical.analysis Learning.indicators 2019 Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Exploring.srl.processes SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Event.sequence Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65 Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? Group.comparison SRL Lms.log.data Trace-quiz Transitional.pattern Visualization.analysis Learning.indicators 2019 Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Event Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Lms.log.data Trace-exercise Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Event Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-feedback Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-reading Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-video Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Method.development SRL Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-video Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Event Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-feedback Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-reading Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-video Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Event.sequence Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Group.event.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Frequent.sequence.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Process.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Process.mining Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Cluster.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66 Analytics of Learning Strategies: Associations with Academic Performance and Feedback Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? Exploring.srl.processes SRL Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Feedback 2019 Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-forum Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-forum Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-forum Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Method.development SRL Performance.measures Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-forum Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-forum Event.sequence Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-forum Group.event.pattern Frequent.sequence.mining Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67 Analytics of Learning Strategies: The Association with the Personality Traits approaches to learning; learning analytics; learning strategies; personality traits RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits? Exploring.srl.processes SRL Performance.measures Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2019 Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68 An application framework for mining online learning processes through event-logs Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? Method.development other Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2019 Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
69 Social Network analysising and academic performance: A longitudinal perspective Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective to better understand the temporal association between SNS use and academic performance. Method.development other Performance.measures Event Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69 Social Network analysising and academic performance: A longitudinal perspective Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective to better understand the temporal association between SNS use and academic performance. Method.development other Performance.measures Time Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69 Social Network analysising and academic performance: A longitudinal perspective Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective to better understand the temporal association between SNS use and academic performance. Method.development other Self-reported Event Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69 Social Network analysising and academic performance: A longitudinal perspective Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective to better understand the temporal association between SNS use and academic performance. Method.development other Self-reported Time Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Customized.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Customized.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Customized.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Customized.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Self-reported Event Group.event.pattern Cluster.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Self-reported Event Group.event.pattern Visualization.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Self-reported Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
70 Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis None Group.comparison game-based learning Self-reported Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2019 Reilly, Joseph M, Dede, Chris
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Event Summative Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Process.mining Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Process.mining Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Basic.statistical.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Basic.statistical.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Learning.indicators 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71 Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse None Method.development collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Course.design 2019 Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
72 Augmenting Knowledge Tracing by Considering Forgetting Behavior deep neural Network analysis; forgetting behavior; knowledge tracing We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. Method.development knowledge tracing Lms.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2019 Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72 Augmenting Knowledge Tracing by Considering Forgetting Behavior deep neural Network analysis; forgetting behavior; knowledge tracing We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. Method.development knowledge tracing Lms.log.data Time Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2019 Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72 Augmenting Knowledge Tracing by Considering Forgetting Behavior deep neural Network analysis; forgetting behavior; knowledge tracing We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. Method.development knowledge tracing Lms.log.data Trace-quiz Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2019 Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Event Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Event Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Event Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Event Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Time Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Time Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Time Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Time Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-other Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-other Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-other Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Lms.log.data Trace-other Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Event Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Event Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Event Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Event Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Time Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Time Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Time Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Time Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-feedback Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-feedback Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-other Group.event.pattern Cluster.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-other Group.event.pattern Cluster.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-other Group.event.pattern Visualization.analysis Course.design 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73 Data-driven unsupervised Cluster analysis of online learner behaviour Experimental; Neurosciences; Psychology; Social Sciences; time-series None Method.development other Performance.measures Trace-other Group.event.pattern Visualization.analysis Feedback 2019 Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Feedback 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Process.mining Learning.indicators 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Process.mining Feedback 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Trace-feedback Transitional.pattern Process.mining Feedback 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74 Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools ITS; exploratory learning; external representation; feedback; maths education None Non-srl.indicators.identification other Lms.log.data Trace-other Transitional.pattern Process.mining Feedback 2019 Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
75 Learning anytime, anywhere: a spatio-temporal analysis for online learning Online course; anytime anywhere; learning performance; spatio-temporal analysis What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern? Group.comparison None Multimodal Event Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
75 Learning anytime, anywhere: a spatio-temporal analysis for online learning Online course; anytime anywhere; learning performance; spatio-temporal analysis What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern? Group.comparison None Multimodal Time Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Event Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76 Investigating students' interaction patterns and dynamic learning sentiments in online discussions Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? Exploring.socio-dynamics affective learning Learning.product Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Performance.measures Event Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Performance.measures Time Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? Method.development None Performance.measures Time Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Performance.measures Event Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Performance.measures Time Summative Other.predictions.models No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77 Transfer Learning Using Representation Learning in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? At-risk.student.identification None Performance.measures Time Summative Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
78 Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses None Method.development None Lms.log.data Time None Other.predictions.models No.learning.focus.outcome 2019 Lee, Jinseok, Yeung, Dit-Yan
78 Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses None Method.development None Lms.log.data Event None Other.predictions.models No.learning.focus.outcome 2019 Lee, Jinseok, Yeung, Dit-Yan
78 Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses None Method.development None Performance.measures Time None Other.predictions.models No.learning.focus.outcome 2019 Lee, Jinseok, Yeung, Dit-Yan
78 Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses None Method.development None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2019 Lee, Jinseok, Yeung, Dit-Yan
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Lms.log.data Event Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Lms.log.data Trace-quiz Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Lms.log.data Trace-reading Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Lms.log.data Trace-exercise Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Lms.log.data Trace-forum Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Performance.measures Event Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Performance.measures Trace-quiz Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Performance.measures Trace-reading Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Performance.measures Trace-exercise Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79 The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses course perfomance; blended learning; temporal pattern How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? Non-srl.indicators.identification other Performance.measures Trace-forum Summative Cluster.analysis Time.on.learning 2019 van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Lms.log.data Event Summative Cluster.analysis No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Learner.characteristics Event Summative Cluster.analysis No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Learner.characteristics Event Summative Other.predictions.models No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Performance.measures Event Summative Cluster.analysis No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80 Mining Activity Log Data to Predict Student's Outcome in a Course Classification; Education data mining; Learning analytics; prediction None At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2019 Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Non-srl.indicators.identification feedback engagement Lms.log.data Event Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Non-srl.indicators.identification feedback engagement Lms.log.data Trace-feedback Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Non-srl.indicators.identification feedback engagement Lms.log.data Trace-other Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Method.development feedback engagement Lms.log.data Event Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Method.development feedback engagement Lms.log.data Trace-feedback Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81 Understanding the process of teachers’ technology adoption with a dynamic analytical model Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. Method.development feedback engagement Lms.log.data Trace-other Summative Basic.statistical.analysis Feedback 2019 Zheng, Longwei, Gibson, David, Gu, Xiaoqing
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-feedback Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82 How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2019 Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
83 Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses Physics; eye-tracking; inquiry; learning analytics; simulation None Non-srl.indicators.identification other Multimodal Event Transitional.pattern Process.mining Learning.indicators 2019 Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83 Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses Physics; eye-tracking; inquiry; learning analytics; simulation None Non-srl.indicators.identification other Multimodal Time Transitional.pattern Process.mining Learning.indicators 2019 Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83 Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses Physics; eye-tracking; inquiry; learning analytics; simulation None Group.comparison other Multimodal Event Transitional.pattern Process.mining Learning.indicators 2019 Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83 Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses Physics; eye-tracking; inquiry; learning analytics; simulation None Group.comparison other Multimodal Time Transitional.pattern Process.mining Learning.indicators 2019 Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Event Event.sequence Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Event Summative Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Event Summative Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Trace-other Event.sequence Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Trace-other Summative Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Customized.log.data Trace-other Summative Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Event Event.sequence Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Event Summative Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Event Summative Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Trace-other Event.sequence Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Trace-other Summative Frequent.sequence.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84 Recognizing patterns of student’s modeling behaviour patterns via process mining Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. Method.development None Performance.measures Trace-other Summative Process.mining Learning.indicators 2019 Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Event Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Event Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-forum Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-forum Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-forum Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-forum Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-quiz Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-quiz Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-quiz Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-quiz Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-reading Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-reading Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-reading Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-other Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-other Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Multimodal Trace-other Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Event Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Event Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-forum Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-forum Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-forum Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-quiz Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-reading Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-reading Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-other Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? Method.development None Lms.log.data Trace-other Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Event Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Event Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-forum Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-forum Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-forum Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-forum Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-quiz Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-quiz Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-quiz Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-quiz Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-reading Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-reading Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-reading Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-other Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-other Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Multimodal Trace-other Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Event Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Event Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-forum Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-forum Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-forum Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-quiz Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-reading Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-reading Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-other Event.sequence Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85 On multi-device use: Using technological modality profiles to explain differences in students’ learning Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? At-risk.student.identification None Lms.log.data Trace-other Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
86 A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86 A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? Non-srl.indicators.identification other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86 A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? Group.comparison other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86 A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? Group.comparison other Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
87 User behavior pattern detection in unstructured processes – a learning management system case study Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? Method.development game-based learning Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Codish, David, Rabin, Eyal, Ravid, Gilad
87 User behavior pattern detection in unstructured processes – a learning management system case study Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? Method.development game-based learning Customized.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2019 Codish, David, Rabin, Eyal, Ravid, Gilad
87 User behavior pattern detection in unstructured processes – a learning management system case study Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? Method.development game-based learning Customized.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2019 Codish, David, Rabin, Eyal, Ravid, Gilad
88 Visual behavior and self-efficacy of game playing: an eye movement analysis Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying. Group.comparison other Multimodal Event Transitional.pattern Process.mining Learning.indicators 2019 Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
88 Visual behavior and self-efficacy of game playing: an eye movement analysis Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying. Group.comparison other Self-reported Event Transitional.pattern Process.mining Learning.indicators 2019 Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Event Summative Content.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Event Summative Visualization.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Time Summative Content.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Time Summative Basic.statistical.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Lms.log.data Time Summative Visualization.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Event Summative Content.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Event Summative Basic.statistical.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Event Summative Visualization.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Time Summative Content.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Time Summative Basic.statistical.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89 Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? Non-srl.indicators.identification affective learning Learning.product Time Summative Visualization.analysis Learning.indicators 2019 Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Lms.log.data Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Performance.measures Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Group.event.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Group.event.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Group.event.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Transitional.pattern Process.mining No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90 Discovery and temporal analysis of MOOC study patterns Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? Method.development None Learner.characteristics Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2019 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
91 Reliable Deep Grade Prediction with Uncertainty Estimation Bayesian Deep Learning; Educational Data Mining; Grade Prediction; Sequential Models; Uncertainty None Method.development None Performance.measures Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Hu, Qian, Rangwala, Huzefa
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Exploring.srl.processes SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Event.sequence Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Event.sequence Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Time.on.learning 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92 Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment STEM education; Self-regulated learning; Sequential mining; Socially shared regulation (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? Group.comparison SSRL; collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Collaboration 2019 Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Transitional.pattern Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Transitional.pattern Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Summative Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Summative Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Event Summative Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Transitional.pattern Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Transitional.pattern Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Transitional.pattern Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Summative Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Summative Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Summative Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Time Summative Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Transitional.pattern Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Transitional.pattern Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Summative Process.mining Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Summative Process.mining Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Summative Basic.statistical.analysis Learning.indicators 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
93 A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? Non-srl.indicators.identification other Customized.log.data Trace-other Summative Basic.statistical.analysis Course.design 2019 Cheng, Kun-Hung, Tsai, Chin-Chung
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2019 Mitra, Ritayan, Chavan, Pankaj
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Event Summative Basic.statistical.analysis Feedback 2019 Mitra, Ritayan, Chavan, Pankaj
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Time Summative Basic.statistical.analysis Learning.indicators 2019 Mitra, Ritayan, Chavan, Pankaj
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Time Summative Basic.statistical.analysis Feedback 2019 Mitra, Ritayan, Chavan, Pankaj
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Trace-feedback Summative Basic.statistical.analysis Learning.indicators 2019 Mitra, Ritayan, Chavan, Pankaj
94 DEBE Feedback for Large Lecture Classroom Analytics Large lectures; learning analytics; live feedback; mobile application; quantified self None Non-srl.indicators.identification affective learning Self-reported Trace-feedback Summative Basic.statistical.analysis Feedback 2019 Mitra, Ritayan, Chavan, Pankaj
95 Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences None Method.development knowledge tracing Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95 Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences None Method.development knowledge tracing Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95 Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences None Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95 Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences None Method.development knowledge tracing Performance.measures Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Time Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Time Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Trace-video Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Lms.log.data Trace-video Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Time Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Time Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Trace-video Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96 Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning None Method.development None Performance.measures Trace-video Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2019 Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
97 Knowledge Tracing with Sequential Key-Value Memory Networks deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models. Method.development knowledge tracing Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Abdelrahman, Ghodai, Wang, Qing
97 Knowledge Tracing with Sequential Key-Value Memory Networks deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models. Method.development knowledge tracing Lms.log.data Trace-quiz Other.sequential.patterns Neural.network No.learning.focus.outcome 2019 Abdelrahman, Ghodai, Wang, Qing
98 Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context Data association; fuzzy ontology; fuzzy reasoning; ontology alignment we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. Method.development None Multimodal Event Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98 Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context Data association; fuzzy ontology; fuzzy reasoning; ontology alignment we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. Method.development None Multimodal Event Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2019 Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98 Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context Data association; fuzzy ontology; fuzzy reasoning; ontology alignment we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. Method.development None Multimodal Time Summative Basic.statistical.analysis No.learning.focus.outcome 2019 Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98 Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context Data association; fuzzy ontology; fuzzy reasoning; ontology alignment we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. Method.development None Multimodal Time Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2019 Wang, Wei, Mu, Wenxin, Gou, Juanqiong
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Lms.log.data Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Performance.measures Time Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Performance.measures Trace-video Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None Method.development None Performance.measures Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Lms.log.data Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Performance.measures Time Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Performance.measures Trace-video Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99 Analysing the predictive power for anticipating assignment grades in a massive open online course None None At-risk.student.identification None Performance.measures Trace-quiz Summative Other.predictions.models No.learning.focus.outcome 2018 Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
100 Investigating temporal access in a flipped classroom: procrastination persists Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction? Group.comparison None Lms.log.data Event Summative Basic.statistical.analysis Time.on.learning 2018 AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100 Investigating temporal access in a flipped classroom: procrastination persists Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction? Group.comparison None Lms.log.data Time Summative Basic.statistical.analysis Time.on.learning 2018 AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100 Investigating temporal access in a flipped classroom: procrastination persists Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction? Group.comparison None Performance.measures Event Summative Basic.statistical.analysis Time.on.learning 2018 AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100 Investigating temporal access in a flipped classroom: procrastination persists Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction? Group.comparison None Performance.measures Time Summative Basic.statistical.analysis Time.on.learning 2018 AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
101 What’s Next? A Recommendation System for Industrial Training Chemistry and Earth Sciences; Computer Science; Industrial trainin; Physics; Statistics for Engineering None Method.development None Learner.characteristics Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Srivastava, Rajiv, Palshikar, Girish Keshav, Chaurasia, Saheb, Dixit, Arati
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Customized.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Customized.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Self-reported Event Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Self-reported Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Multimodal Event Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
102 Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining None Exploring.srl.processes srl; affective learning; game-based learning Multimodal Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2018 Taub, Michelle, Azevedo, Roger
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Event Other.sequential.patterns Network.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Event Other.sequential.patterns Content.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Event Other.sequential.patterns Visualization.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Trace-forum Other.sequential.patterns Network.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Trace-forum Other.sequential.patterns Content.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Trace-forum Other.sequential.patterns Visualization.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Network.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Content.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Visualization.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Network.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Content.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103 A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Visualization.analysis Learning.indicators 2018 Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Lms.log.data Event Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Lms.log.data Time Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Performance.measures Event Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Performance.measures Time Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Learner.characteristics Event Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104 A Sequence Data Model for Analyzing Temporal Patterns of Student Data Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling None Method.development None Learner.characteristics Time Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2018 Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
105 Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension None Method.development other Learning.product Trace-reading Summative Neural.network Learning.indicators 2018 Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105 Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension None Method.development other Learning.product Trace-reading Summative Content.analysis Learning.indicators 2018 Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105 Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension None Method.development other Learning.product Trace-quiz Summative Neural.network Learning.indicators 2018 Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105 Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension None Method.development other Learning.product Trace-quiz Summative Content.analysis Learning.indicators 2018 Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Event Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-reading Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-reading Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-quiz Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Customized.log.data Trace-quiz Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Event Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Event Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-reading Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-reading Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-reading Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-reading Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-quiz Summative Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-quiz Summative Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-quiz Transitional.pattern Process.mining Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106 Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis None Non-srl.indicators.identification motivation Self-reported Trace-quiz Transitional.pattern Basic.statistical.analysis Learning.indicators 2018 Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
107 Understanding user behavioral patterns in open knowledge communities Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis None Non-srl.indicators.identification collaborative knowledge building Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107 Understanding user behavioral patterns in open knowledge communities Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis None Non-srl.indicators.identification collaborative knowledge building Customized.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2018 Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107 Understanding user behavioral patterns in open knowledge communities Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis None Non-srl.indicators.identification collaborative knowledge building Customized.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107 Understanding user behavioral patterns in open knowledge communities Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis None Non-srl.indicators.identification collaborative knowledge building Customized.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2018 Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
108 Predicting Learning Difficulty Based on Gaze and Pupil Response e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis None Method.development other Multimodal Time None Other.predictions.models Time.on.learning 2018 Parikh, Saurin, Kalva, Hari
108 Predicting Learning Difficulty Based on Gaze and Pupil Response e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis None Method.development other Multimodal Trace-reading None Other.predictions.models Time.on.learning 2018 Parikh, Saurin, Kalva, Hari
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Customized.log.data Trace-reading Other.sequential.patterns Other.predictions.models Course.design 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Customized.log.data Trace-reading Other.sequential.patterns Other.predictions.models Feedback 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Customized.log.data Trace-other Other.sequential.patterns Other.predictions.models Course.design 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Customized.log.data Trace-other Other.sequential.patterns Other.predictions.models Feedback 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Learning.product Trace-reading Other.sequential.patterns Other.predictions.models Course.design 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Learning.product Trace-reading Other.sequential.patterns Other.predictions.models Feedback 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Learning.product Trace-other Other.sequential.patterns Other.predictions.models Course.design 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109 Social tagging strategy for enhancing e-learning experience Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies None Method.development collaborative knowledge building Learning.product Trace-other Other.sequential.patterns Other.predictions.models Feedback 2018 Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
110 How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities FLOSS; learning across scales; situated cognition None Method.development collaborative knowledge building Contextual Time None Qualitative.analysis Course.design 2018 Johri, Aditya
110 How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities FLOSS; learning across scales; situated cognition None Method.development collaborative knowledge building Contextual Time None Qualitative.analysis Time.on.learning 2018 Johri, Aditya
111 Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect None Non-srl.indicators.identification other Lms.log.data Time Summative Basic.statistical.analysis Time.on.learning 2018 Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
111 Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect None Non-srl.indicators.identification other Lms.log.data Time Summative Visualization.analysis Time.on.learning 2018 Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
112 Observational Scaffolding for Learning Analytics: A Methodological Proposal Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics None Method.development collaborative knowledge building Customized.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2018 Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
112 Observational Scaffolding for Learning Analytics: A Methodological Proposal Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2018 Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Event Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Event Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Time Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Time Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-reading Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-reading Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-forum Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-forum Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-other Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
113 Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours Learning analytics; engagement; learning design; temporal analysis; time management None Non-srl.indicators.identification time management Lms.log.data Trace-other Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
114 Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2018 Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
114 Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students None At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2018 Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Event Summative Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Event Summative Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Non-srl.indicators.identification other Lms.log.data Trace-forum Summative Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Event Transitional.pattern Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Event Summative Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Event Summative Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Trace-forum Transitional.pattern Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115 Effects of success v failure cases on learner-learner interaction Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning None Group.comparison other Lms.log.data Trace-forum Summative Content.analysis Learning.indicators 2018 Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Event Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Event Summative Basic.statistical.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Event Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Event Summative Visualization.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Time Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Time Summative Basic.statistical.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Time Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Non-srl.indicators.identification time on task Lms.log.data Time Summative Visualization.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Event Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Event Summative Basic.statistical.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Event Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Event Summative Visualization.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Time Summative Basic.statistical.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Time Summative Basic.statistical.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Time Summative Visualization.analysis Course.design 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
116 Linking Students' Timing of Engagement to Learning Design and Academic Performance engagement; higher education; learning analytics; learning design; temporal; virtual learning environment None Group.comparison time on task Lms.log.data Time Summative Visualization.analysis Time.on.learning 2018 Nguyen, Quan, Huptych, Michal, Rienties, Bart
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Summative Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Summative Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Transitional.pattern Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Summative Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Summative Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None Group.comparison None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Transitional.pattern Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Transitional.pattern Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Transitional.pattern Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Summative Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Summative Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Event Summative Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Transitional.pattern Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Transitional.pattern Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Transitional.pattern Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Summative Process.mining No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Summative Cluster.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117 Temporal Dynamics of MOOC Learning Trajectories MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining None At-risk.student.identification None Lms.log.data Time Summative Visualization.analysis No.learning.focus.outcome 2018 Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Customized.log.data Event None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Customized.log.data Trace-video None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Customized.log.data Trace-other None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Multimodal Event None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Multimodal Trace-video None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
118 A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning None Method.development None Multimodal Trace-other None Basic.statistical.analysis Course.design 2018 Liu, Ran, Stamper, John C, Davenport, Jodi
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-other Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-other Transitional.pattern Frequent.sequence.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
119 Behavioral patterns of knowledge construction in online cooperative translation activities Behavioral pattern; Cooperative translation; Engagement; Knowledge construction None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-other Transitional.pattern Process.mining Collaboration 2018 Yang, Xianmin, Li, Jihong, Xing, Beibei
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Event Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Time Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Non-srl.indicators.identification time on task Lms.log.data Trace-video Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Event Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Time Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Group.event.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Group.event.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Group.event.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Transitional.pattern Process.mining Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Transitional.pattern Cluster.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120 Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis None Method.development time on task Lms.log.data Trace-video Transitional.pattern Visualization.analysis Feedback 2018 Boroujeni, Mina Shirvani, Dillenbourg, Pierre
121 Video-Based Question Generation for Mobile Learning Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment None Method.development None Customized.log.data Event Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2018 Nimkanjana, Klinsukon, Witosurapot, Suntorn
121 Video-Based Question Generation for Mobile Learning Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment None Method.development None Customized.log.data Trace-video Other.sequential.patterns Basic.statistical.analysis No.learning.focus.outcome 2018 Nimkanjana, Klinsukon, Witosurapot, Suntorn
122 Representing and Predicting Student Navigational Pathways in Online College Courses long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling None Method.development None Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2018 Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122 Representing and Predicting Student Navigational Pathways in Online College Courses long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2018 Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122 Representing and Predicting Student Navigational Pathways in Online College Courses long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Neural.network No.learning.focus.outcome 2018 Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122 Representing and Predicting Student Navigational Pathways in Online College Courses long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2018 Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Event Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Event Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Event Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Event Summative Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Trace-forum Summative Process.mining Learning.indicators 2018 Bakla, Arif
123 Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation None Group.comparison collaborative knowledge building Learning.product Trace-forum Summative Process.mining Collaboration 2018 Bakla, Arif
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Lms.log.data Event Other.sequential.patterns Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Other.sequential.patterns Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Lms.log.data Trace-forum Summative Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Learning.product Event Other.sequential.patterns Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Learning.product Event Summative Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
124 Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis None Group.comparison collaborative knowledge building Learning.product Trace-forum Summative Basic.statistical.analysis Learning.indicators 2018 Chiu, MIng Ming
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Event Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-quiz Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-quiz Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-reading Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-reading Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-exercise Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-exercise Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-forum Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Method.development None Customized.log.data Trace-forum Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Event Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-quiz Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-quiz Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-reading Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-reading Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-exercise Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-exercise Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-forum Event.sequence Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125 An empirical study of using sequential behavior pattern mining approach to predict learning styles Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining None Non-srl.indicators.identification None Customized.log.data Trace-forum Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2018 Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Performance.measures Time Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Learner.characteristics Event Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126 Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education None At-risk.student.identification None Learner.characteristics Time Summative Other.predictions.models No.learning.focus.outcome 2018 Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Lms.log.data Event Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Lms.log.data Time Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Lms.log.data Trace-quiz Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Lms.log.data Trace-reading Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Performance.measures Event Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Performance.measures Time Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Performance.measures Trace-quiz Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Performance.measures Trace-reading Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Learner.characteristics Event Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Learner.characteristics Time Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Learner.characteristics Trace-quiz Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127 Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success higher education; learning analytics; predictive modeling; self-regulated learning; student engagement None At-risk.student.identification srl Learner.characteristics Trace-reading Summative Basic.statistical.analysis Time.on.learning 2017 Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Customized.log.data Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Customized.log.data Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Customized.log.data Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Customized.log.data Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Performance.measures Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Performance.measures Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Performance.measures Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Performance.measures Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Self-reported Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Self-reported Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Self-reported Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Time.to.intervention srl Self-reported Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Customized.log.data Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Customized.log.data Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Customized.log.data Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Customized.log.data Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Performance.measures Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Performance.measures Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Performance.measures Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Performance.measures Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Self-reported Event Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Self-reported Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Self-reported Time Other.sequential.patterns Other.predictions.models Time.on.learning 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128 Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning None Method.development srl Self-reported Time Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Lms.log.data Event Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Lms.log.data Time Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Self-reported Event Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Self-reported Time Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Learner.characteristics Event Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Learner.characteristics Time Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129 Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study Adult learning; Distance education and telelearning; Lifelong learning None Non-srl.indicators.identification other Performance.measures Time Summative Basic.statistical.analysis Learning.indicators 2017 Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Method.development None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Method.development None Performance.measures Event None Cluster.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Method.development None Performance.measures Event None Visualization.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None At-risk.student.identification None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None At-risk.student.identification None Performance.measures Event None Cluster.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None At-risk.student.identification None Performance.measures Event None Visualization.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Group.comparison None Performance.measures Event None Other.predictions.models No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Group.comparison None Performance.measures Event None Cluster.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130 Analyzing undergraduate students' performance using educational data mining Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes None Group.comparison None Performance.measures Event None Visualization.analysis No.learning.focus.outcome 2017 Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Event Other.sequential.patterns Network.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Event Other.sequential.patterns Network.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Event Other.sequential.patterns Visualization.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Event Other.sequential.patterns Visualization.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Trace-other Other.sequential.patterns Network.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Trace-other Other.sequential.patterns Network.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Trace-other Other.sequential.patterns Visualization.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Trace-other Other.sequential.patterns Visualization.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Time Other.sequential.patterns Network.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Time Other.sequential.patterns Network.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Time Other.sequential.patterns Visualization.analysis Learning.indicators 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131 Gaining Insight by Transforming Between Temporal Representations of Human Interaction Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses None Non-srl.indicators.identification affective learning; collaborative knowledge building Contextual Time Other.sequential.patterns Visualization.analysis Collaboration 2017 Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Event Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-other Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-video Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Trace-forum Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Customized.log.data Time Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Event Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-other Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-video Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Trace-forum Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Transitional.pattern Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Transitional.pattern Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Transitional.pattern Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Summative Process.mining Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Summative Basic.statistical.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132 Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data None Exploring.srl.processes srl; collaborative knowledge building; affective learning Learning.product Time Summative Visualization.analysis Collaboration 2017 Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Event Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Event Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-exercise Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-exercise Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-reading Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-reading Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-reading Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-other Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Customized.log.data Trace-other Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Event Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Event Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-exercise Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-exercise Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-exercise Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-reading Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-reading Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-reading Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-reading Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-other Transitional.pattern Process.mining Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133 Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis None Group.comparison other Performance.measures Trace-other Transitional.pattern Visualization.analysis Collaboration 2017 Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Event Event.sequence Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Event Event.sequence Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Trace-forum Event.sequence Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Event Event.sequence Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Event Event.sequence Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Trace-forum Event.sequence Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Trace-forum Event.sequence Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Frequent.sequence.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134 Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics None Method.development collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Time.on.learning 2017 Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Lms.log.data Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Method.development other Performance.measures Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Lms.log.data Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None At-risk.student.identification other Performance.measures Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Lms.log.data Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Event Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Time Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Visualization.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Visualization.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Network.analysis Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Network.analysis Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Other.predictions.models Time.on.learning 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135 An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments dashboards; introductory programming; learning analytics; machine learning; peer tutors None Time.to.intervention other Performance.measures Trace-other Summative Other.predictions.models Collaboration 2017 Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
136 Using Programming Process Data to Detect Differences in Students' Patterns of Programming educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model None Method.development None Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Carter, Adam Scott, Hundhausen, Christopher David
136 Using Programming Process Data to Detect Differences in Students' Patterns of Programming educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model None Method.development None Lms.log.data Event Summative Frequent.sequence.mining No.learning.focus.outcome 2017 Carter, Adam Scott, Hundhausen, Christopher David
136 Using Programming Process Data to Detect Differences in Students' Patterns of Programming educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model None Method.development None Performance.measures Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Carter, Adam Scott, Hundhausen, Christopher David
136 Using Programming Process Data to Detect Differences in Students' Patterns of Programming educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model None Method.development None Performance.measures Event Summative Frequent.sequence.mining No.learning.focus.outcome 2017 Carter, Adam Scott, Hundhausen, Christopher David
137 A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics None Method.development other Multimodal Event Transitional.pattern Process.mining Time.on.learning 2017 Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137 A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics None Method.development other Multimodal Event Transitional.pattern Visualization.analysis Time.on.learning 2017 Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137 A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics None Method.development other Multimodal Trace-other Transitional.pattern Process.mining Time.on.learning 2017 Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137 A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics None Method.development other Multimodal Trace-other Transitional.pattern Visualization.analysis Time.on.learning 2017 Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Lms.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Method.development srl Performance.measures Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Lms.log.data Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-reading Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-quiz Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-video Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Event.sequence Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Event.sequence Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Group.event.pattern Cluster.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138 Learning analytics to unveil learning strategies in a flipped classroom learning strategies; sequence analysis; self-regulated learning; learning analytics None Exploring.srl.processes srl Performance.measures Trace-other Group.event.pattern Visualization.analysis Learning.indicators 2017 Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
139 Shapes of Educational Data in an Online Calculus Course Markov chain; clickstream; sequence analysis None Method.development None Lms.log.data Event Event.sequence Other.predictions.models Learning.indicators 2017 Caprotti, Olga
139 Shapes of Educational Data in an Online Calculus Course Markov chain; clickstream; sequence analysis None Method.development None Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2017 Caprotti, Olga
139 Shapes of Educational Data in an Online Calculus Course Markov chain; clickstream; sequence analysis None Method.development None Lms.log.data Event Other.sequential.patterns Other.predictions.models Learning.indicators 2017 Caprotti, Olga
139 Shapes of Educational Data in an Online Calculus Course Markov chain; clickstream; sequence analysis None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis Learning.indicators 2017 Caprotti, Olga
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Content.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Cluster.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Cluster.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
140 Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Group.event.pattern Visualization.analysis Learning.indicators 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Event Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-exercise Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-reading Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-video Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Lms.log.data Trace-quiz Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Event Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-exercise Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-reading Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-video Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Non-srl.indicators.identification other Self-reported Trace-quiz Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Event Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-exercise Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-reading Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-video Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Lms.log.data Trace-quiz Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Event Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-exercise Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-reading Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-video Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Summative Frequent.sequence.mining Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141 Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance Learning analytics; approaches to learning; learning strategy; self-reported measures None Group.comparison other Self-reported Trace-quiz Summative Cluster.analysis Learning.indicators 2017 Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Transitional.pattern Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Transitional.pattern Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Summative Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Summative Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Summative Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Summative Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Summative Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.socio-dynamics srl; collaborative knowledge building Contextual Trace-other Summative Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Transitional.pattern Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Transitional.pattern Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Summative Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Summative Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Event Summative Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Transitional.pattern Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Summative Qualitative.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Summative Qualitative.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Summative Basic.statistical.analysis Collaboration 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142 Co-regulation and knowledge construction in an online synchronous problem based learning setting Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning None Exploring.srl.processes srl; collaborative knowledge building Contextual Trace-other Summative Basic.statistical.analysis Learning.indicators 2017 Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Event Summative Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Event Summative Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Event Group.event.pattern Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Event Group.event.pattern Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Trace-forum Summative Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Trace-forum Summative Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Trace-forum Group.event.pattern Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Exploring.socio-dynamics other Learning.product Trace-forum Group.event.pattern Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Event Summative Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Event Summative Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Event Group.event.pattern Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Event Group.event.pattern Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Trace-forum Summative Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Trace-forum Summative Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Trace-forum Group.event.pattern Frequent.sequence.mining Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143 Role Modelling in MOOC Discussion Forums Discussion Forums; Social Network Analysis; Temporal data None Non-srl.indicators.identification other Learning.product Trace-forum Group.event.pattern Cluster.analysis Course.design 2017 Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Group.event.pattern Cluster.analysis No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Group.event.pattern Process.mining No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Group.event.pattern Visualization.analysis No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Other.sequential.patterns Process.mining No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
144 Mining frequent learning pathways from a large educational dataset Graph mining; Learning pathways; Process.mining; Sequence mining None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2017 Patel, Nirmal, Sellman, Collin, Lomas, Derek
145 Sequence modelling for analysing student interaction with educational systems Clustering; Markov chains; Sequence modelling None Method.development None Lms.log.data Event Other.sequential.patterns Cluster.analysis No.learning.focus.outcome 2017 Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145 Sequence modelling for analysing student interaction with educational systems Clustering; Markov chains; Sequence modelling None Method.development None Lms.log.data Event Other.sequential.patterns Process.mining No.learning.focus.outcome 2017 Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145 Sequence modelling for analysing student interaction with educational systems Clustering; Markov chains; Sequence modelling None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2017 Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Time Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-video Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Time Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Summative Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Summative Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Summative Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Summative Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Group.event.pattern Cluster.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Group.event.pattern Cluster.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Group.event.pattern Visualization.analysis Time.on.learning 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146 Dynamics of MOOC Discussion Forums MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-video Group.event.pattern Visualization.analysis Collaboration 2017 Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Event None Visualization.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Event None Visualization.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Event None Basic.statistical.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Event None Basic.statistical.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-reading None Visualization.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-reading None Visualization.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-reading None Basic.statistical.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-reading None Basic.statistical.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-quiz None Visualization.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-quiz None Visualization.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-quiz None Basic.statistical.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Trace-quiz None Basic.statistical.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Time None Visualization.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Time None Visualization.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Time None Basic.statistical.analysis Time.on.learning 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147 What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment None Method.development other Customized.log.data Time None Basic.statistical.analysis Learning.indicators 2017 Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Event Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-exercise Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-reading Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-other Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Lms.log.data Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Event Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-exercise Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-exercise Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-reading Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-reading Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-other Event.sequence Content.analysis No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148 Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics None Method.development other Learning.product Trace-other Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2017 Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Transitional.pattern Visualization.analysis Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Visualization.analysis Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Transitional.pattern Visualization.analysis Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Transitional.pattern Visualization.analysis Learning.indicators 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149 Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Transitional.pattern Visualization.analysis Collaboration 2017 Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Learning.product Event Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Learning.product Time Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150 A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions None Non-srl.indicators.identification other Learning.product Trace-forum Transitional.pattern Process.mining Learning.indicators 2017 Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
151 Students' Careers Analysis: A Process Mining Approach educational mining; process mining; students' career analysis None Method.development None Performance.measures Event Summative Process.mining No.learning.focus.outcome 2017 Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151 Students' Careers Analysis: A Process Mining Approach educational mining; process mining; students' career analysis None Method.development None Performance.measures Event Summative Visualization.analysis No.learning.focus.outcome 2017 Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151 Students' Careers Analysis: A Process Mining Approach educational mining; process mining; students' career analysis None Method.development None Performance.measures Time Summative Process.mining No.learning.focus.outcome 2017 Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151 Students' Careers Analysis: A Process Mining Approach educational mining; process mining; students' career analysis None Method.development None Performance.measures Time Summative Visualization.analysis No.learning.focus.outcome 2017 Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
152 Understanding Student Interactions in Capstone Courses to Improve Learning Experiences capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning None Method.development None Customized.log.data Event Transitional.pattern Process.mining Course.design 2017 Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152 Understanding Student Interactions in Capstone Courses to Improve Learning Experiences capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning None Method.development None Customized.log.data Event Transitional.pattern Visualization.analysis Course.design 2017 Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152 Understanding Student Interactions in Capstone Courses to Improve Learning Experiences capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning None Method.development None Learner.characteristics Event Transitional.pattern Process.mining Course.design 2017 Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152 Understanding Student Interactions in Capstone Courses to Improve Learning Experiences capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning None Method.development None Learner.characteristics Event Transitional.pattern Visualization.analysis Course.design 2017 Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Lms.log.data Trace-other Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Other.sequential.patterns Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Other.sequential.patterns Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Other.sequential.patterns Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Summative Content.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Summative Network.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
153 Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-other Summative Visualization.analysis Collaboration 2017 Lee, Alwyn Vwen Yen, Tan, Seng Chee
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Event Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Time Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Lms.log.data Trace-forum Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Learning.product Time Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154 Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study Blog; Collaborative learning; Behavioral pattern; Instructional strategy None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Course.design 2017 Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Customized.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Customized.log.data Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155 Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies None Non-srl.indicators.identification game-based learning Performance.measures Trace-other Transitional.pattern Visualization.analysis Learning.indicators 2017 Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
156 Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving None None Non-srl.indicators.identification game-based learning Customized.log.data Event Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Chen, Chih-Hung
156 Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving None None Non-srl.indicators.identification game-based learning Customized.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Chen, Chih-Hung
156 Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving None None Non-srl.indicators.identification game-based learning Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Chen, Chih-Hung
156 Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving None None Non-srl.indicators.identification game-based learning Performance.measures Trace-other Transitional.pattern Process.mining Learning.indicators 2017 Hwang, Gwo-Jen, Chen, Chih-Hung
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Event Transitional.pattern Process.mining Learning.indicators 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Event Transitional.pattern Process.mining Collaboration 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Trace-forum Transitional.pattern Process.mining Learning.indicators 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Trace-forum Transitional.pattern Process.mining Collaboration 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Time Transitional.pattern Process.mining Learning.indicators 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157 Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis None Exploring.srl.processes SRL Contextual Time Transitional.pattern Process.mining Collaboration 2017 Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Event Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Trace-forum Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Customized.log.data Trace-exercise Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Learning.product Event Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-forum Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158 An analysis of student collaborative problem solving activities mediated by collaborative simulations Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation None Exploring.socio-dynamics collaborative knowledge building Learning.product Trace-exercise Transitional.pattern Process.mining Collaboration 2017 Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Lms.log.data Event Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Lms.log.data Trace-forum Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Lms.log.data Time Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Learning.product Event Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Learning.product Trace-forum Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159 The Changing Patterns of MOOC Discourse discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion None Exploring.socio-dynamics None Learning.product Time Summative Basic.statistical.analysis No.learning.focus.outcome 2017 Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
160 Modeling Student Learning Styles in MOOCs behavior modeling; moocs; probabilistic modeling; sequential data mining None Method.development None Lms.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2017 Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160 Modeling Student Learning Styles in MOOCs behavior modeling; moocs; probabilistic modeling; sequential data mining None Method.development None Lms.log.data Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2017 Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160 Modeling Student Learning Styles in MOOCs behavior modeling; moocs; probabilistic modeling; sequential data mining None Method.development None Learning.product Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2017 Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160 Modeling Student Learning Styles in MOOCs behavior modeling; moocs; probabilistic modeling; sequential data mining None Method.development None Learning.product Event Other.sequential.patterns Visualization.analysis No.learning.focus.outcome 2017 Shi, Yuling, Peng, Zhiyong, Wang, Hongning
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Lms.log.data Event Event.sequence Cluster.analysis No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Lms.log.data Event Event.sequence Other.predictions.models No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Performance.measures Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Performance.measures Event Event.sequence Cluster.analysis No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Method.development None Performance.measures Event Event.sequence Other.predictions.models No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Lms.log.data Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Lms.log.data Event Event.sequence Cluster.analysis No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Lms.log.data Event Event.sequence Other.predictions.models No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Performance.measures Event Event.sequence Frequent.sequence.mining No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Performance.measures Event Event.sequence Cluster.analysis No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
161 Data-driven modeling of learners' individual differences for predicting engagement and success in online learning Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining None Group.comparison None Performance.measures Event Event.sequence Other.predictions.models No.learning.focus.outcome 2021 Akhuseyinoglu, Kamil, Brusilovsky, Peter
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Customized.log.data Event Summative Basic.statistical.analysis Time.on.learning 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Customized.log.data Event Summative Basic.statistical.analysis Course.design 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Customized.log.data Time Summative Basic.statistical.analysis Time.on.learning 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Customized.log.data Time Summative Basic.statistical.analysis Course.design 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Performance.measures Event Summative Basic.statistical.analysis Time.on.learning 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Performance.measures Event Summative Basic.statistical.analysis Course.design 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Performance.measures Time Summative Basic.statistical.analysis Time.on.learning 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162 Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments Dashboard; EVE; LMS; learning; personalized; teaching None Method.development None Performance.measures Time Summative Basic.statistical.analysis Course.design 2021 Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Lms.log.data Trace-quiz Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Lms.log.data Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Learner.characteristics Event Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Learner.characteristics Trace-quiz Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Learner.characteristics Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Learner.characteristics Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Performance.measures Trace-quiz Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Performance.measures Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163 Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education None Non-srl.indicators.identification other Performance.measures Trace-feedback Transitional.pattern Process.mining Learning.indicators 2021 Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Customized.log.data Event Summative Process.mining Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Customized.log.data Trace-exercise Summative Process.mining Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Customized.log.data Trace-exercise Summative Basic.statistical.analysis Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Performance.measures Event Summative Process.mining Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Performance.measures Trace-exercise Summative Process.mining Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164 Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) None Exploring.srl.processes SRL Performance.measures Trace-exercise Summative Basic.statistical.analysis Learning.indicators 2021 Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Event.sequence Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165 Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data Learning pathways; Process.mining; Self-regulated learning None Exploring.srl.processes SRL Lms.log.data Trace-other Transitional.pattern Cluster.analysis Learning.indicators 2021 Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Event Transitional.pattern Process.mining Course.design 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Event Transitional.pattern Process.mining Feedback 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Trace-forum Transitional.pattern Process.mining Course.design 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Trace-forum Transitional.pattern Process.mining Feedback 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Trace-feedback Transitional.pattern Process.mining Course.design 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166 Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) None Non-srl.indicators.identification other Contextual Trace-feedback Transitional.pattern Process.mining Feedback 2021 Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-forum Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Non-srl.indicators.identification other Lms.log.data Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-exercise Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-forum Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Event.sequence Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Event.sequence Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Event.sequence Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Transitional.pattern Process.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167 Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming automated assessment; computer science; learning analytics; process mining; programming; sequence mining None Method.development other Lms.log.data Trace-video Transitional.pattern Cluster.analysis Learning.indicators 2021 Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
168 Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis None Non-srl.indicators.identification collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Network.analysis Collaboration 2021 Ouyang, Fan
168 Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis None Method.development collaborative knowledge building Learning.product Trace-forum Other.sequential.patterns Network.analysis Collaboration 2021 Ouyang, Fan
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Customized.log.data Event Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Customized.log.data Trace-other Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Customized.log.data Trace-other Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Performance.measures Event Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Performance.measures Trace-other Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Non-srl.indicators.identification other Performance.measures Trace-other Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Customized.log.data Event Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Customized.log.data Event Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Customized.log.data Trace-other Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Customized.log.data Trace-other Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Performance.measures Event Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Performance.measures Event Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Performance.measures Trace-other Event.sequence Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169 Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining None Method.development other Performance.measures Trace-other Summative Basic.statistical.analysis Learning.indicators 2021 He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Lms.log.data Time Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Non-srl.indicators.identification other Performance.measures Time Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Event Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Lms.log.data Time Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Event Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Event.sequence Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Event.sequence Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Event.sequence Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Event.sequence Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Transitional.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Transitional.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Transitional.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Transitional.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Group.event.pattern Process.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Group.event.pattern Cluster.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
170 The longitudinal trajectories of online engagement over a full program Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement None Method.development other Performance.measures Time Group.event.pattern Visualization.analysis Learning.indicators 2021 Saqr, Mohammed, Lopez-Pernas, Sonsoles
171 Visual search patterns, information selection strategies, and information anxiety for online information problem solving Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies None Non-srl.indicators.identification other Multimodal Event Transitional.pattern Process.mining Learning.indicators 2021 Tsai, Meng Jung, Wu, An Hsuan
171 Visual search patterns, information selection strategies, and information anxiety for online information problem solving Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies None Non-srl.indicators.identification other Multimodal Trace-other Transitional.pattern Process.mining Learning.indicators 2021 Tsai, Meng Jung, Wu, An Hsuan
172 Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior None Method.development None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2021 El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172 Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior None Method.development None Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2021 El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172 Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior None Method.development None Performance.measures Event Summative Other.predictions.models No.learning.focus.outcome 2021 El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172 Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior None Method.development None Performance.measures Trace-video Summative Other.predictions.models No.learning.focus.outcome 2021 El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Exploring.srl.processes SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Event Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Lms.log.data Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Event Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Event Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Event Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Event Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Trace-quiz Event.sequence Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Trace-quiz Event.sequence Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Trace-quiz Group.event.pattern Frequent.sequence.mining Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173 Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining None Group.comparison SRL Performance.measures Trace-quiz Group.event.pattern Cluster.analysis Learning.indicators 2021 Zheng, Juan, Li, Shan, Lajoie, Susanne P.
174 Learner behavior prediction in a learning management system Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2021 Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
174 Learner behavior prediction in a learning management system Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis None At-risk.student.identification None Lms.log.data Time Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2021 Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Event Summative Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Event Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Time Summative Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Time Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Trace-video Summative Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175 Predictive learning analytics using deep learning model in MOOCs’ courses videos Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream None At-risk.student.identification None Lms.log.data Trace-video Other.sequential.patterns Other.predictions.models No.learning.focus.outcome 2021 Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
176 Understanding students’ behavioural intention to use facebook as a supplementary learning platform: A mixed methods approach Facebook; Mixed methods; Online supplementary learning platform; Perceived enjoyment; Technology acceptance None Non-srl.indicators.identification affective learning Contextual Event Transitional.pattern Basic.statistical.analysis Learning.indicators 2021 Hoi, Vo Ngoc, Hang, Ho Le